AI & Machine Learning Course | Python, Deep Learning, NLP Training AKIRA Global Technologies

AI & Machine Learning Course

60 Hours

Live Online

Placement Assistance

Become a job-ready AI/ML professional with hands-on labs, real-world datasets, and an end-to-end capstone project, expert mentorship, and a recognized AI and Machine Learning certification training course.

About the AI & Machine Learning Full Course

AKIRA Global Technologies AI and Machine Learning course takes learners from AI/ML fundamentals to building, evaluating, and deploying real-world machine learning and deep learning models in just 60 hours. This AI/ML full course covers Python programming, mathematics and statistics for machine learning, data preprocessing and exploratory data analysis, supervised and unsupervised learning, model evaluation and tuning, deep learning and neural networks, natural language processing, computer vision, and model deployment with MLOps basics.

Whether you’re an engineering or CS student, a working professional transitioning into Data Science or AI, or an analyst looking to upskill into machine learning, this AI and Machine Learning certification training course is 100% hands-on, with every concept reinforced through a coding exercise in Python. The program uses industry-relevant datasets across finance, healthcare, retail, text, and image data, and covers the complete ML lifecycle from data cleaning to model deployment.

This isn’t just an AI/ML certification course on paper, it’s a practical, project-focused program that culminates in an end-to-end capstone project participants build and present, so you finish with GitHub-ready projects and are prepared to apply for Machine Learning Engineer, AI Engineer, and Data Scientist roles.

Course Details	Information
Duration	60 Hours
Mode	Classroom / Live Online / Hybrid
Format	Lectures + Hands-on Labs + Case Studies + Capstone Project
Level	Beginner to Intermediate
Certification	Certificate of Completion on Project Submission
Tools Covered	Python, Jupyter/Colab, NumPy, Pandas, Matplotlib/Seaborn, Scikit-learn, TensorFlow/Keras, PyTorch, NLTK/spaCy, OpenCV, Flask/FastAPI, Git/GitHub, AWS/GCP/Streamlit

Who Should Enroll?

Engineering/CS Students & Fresh Graduates

Students who want a strong, structured foundation in AI and Machine Learning from day one

Working Professionals

Professionals transitioning into Data Science or AI roles

Analysts

Analysts looking to upskill into Machine Learning

What You'll Get

When you join Akira Global Technology’s AI & Machine Learning Course, you get complete support at every step, from learning to landing a job.

Training

Python for AI/ML Training (6 Hrs)
Math, Statistics & Classical ML Training (23 Hrs)
Deep Learning, NLP & Computer Vision Training (16 Hrs)
Deployment & MLOps Training (4 Hrs)

Hands-on Experience

100% Hands-On Labs on Every Module
Industry-Relevant Datasets (Finance, Healthcare, Retail, Text, Image)
1 End-to-End Capstone Project

Get Certified

Certificate of Completion
Capstone Project Presentation
GitHub-Ready Project Portfolio

Interview Preparation

Resume/Portfolio Guidance
Common ML Interview Questions
Mock Interviews & Doubt-Clearing Sessions

Placements

Placement Assistance
Interview Calls
Guidance for ML Engineer, AI Engineer, and Data Scientist roles

Career Assistance

Placement Support & Resume Building
Portfolio Review
Mentorship Sessions Built Into Lab Hours

AI & Machine Learning Course Syllabus

Our AI and Machine Learning course syllabus runs for 60 hours, moving from AI/ML fundamentals through Python, mathematics and statistics, data preprocessing, supervised and unsupervised learning, model evaluation, deep learning, NLP, computer vision, and deployment, before wrapping up with a capstone project.

Prerequisites:

Basic programming knowledge (any language); Python familiarity is a plus but not mandatory
High-school level mathematics (algebra, basic probability)
A laptop with internet access (or use of the provided cloud lab environment)

Module	Duration	Topics Covered	Tools/Project
Module 1: Introduction to Artificial Intelligence & Machine Learning	2 Hours	What is AI, ML & Deep Learning and How They Relate Types of Machine Learning (Supervised, Unsupervised, Reinforcement Learning) Real-World AI/ML Applications AI/ML Project Lifecycle Overview Setting Up the Python Environment (Anaconda/Colab)	-
Module 2: Python Programming for AI/ML	6 Hours	Python Basics Refresher (Data Types, Control Flow, Functions) NumPy (Arrays, Vectorization, Broadcasting) Pandas (Series, DataFrames, Indexing, Filtering, Grouping) Data Visualization with Matplotlib & Seaborn	Hands-on Lab: Exploring a real dataset using Pandas & visualization
Module 3: Mathematics & Statistics for Machine Learning	6 Hours	Linear Algebra Essentials (Vectors, Matrices, Matrix Operations) Probability Fundamentals (Distributions, Conditional Probability, Bayes' Theorem) Descriptive Statistics (Mean, Variance, Standard Deviation, Correlation) Introduction to Calculus for ML (Derivatives & Gradients) Hypothesis Testing Basics	Hands-on Lab: Statistical analysis on a sample dataset
Module 4: Data Preprocessing & Exploratory Data Analysis (EDA)	5 Hours	Handling Missing Values, Outliers & Duplicates Data Encoding (Label Encoding, One-Hot Encoding) Feature Scaling (Normalization vs Standardization) Exploratory Data Analysis Techniques & Storytelling with Data Train-Test Split & Cross-Validation Basics	Hands-on Lab: Full EDA and cleaning pipeline on a raw dataset
Module 5: Supervised Learning Algorithms	8 Hours	Linear Regression & Multiple Linear Regression Logistic Regression for Classification Decision Trees Random Forests & Ensemble Basics (Bagging) Support Vector Machines (SVM) K-Nearest Neighbors (KNN) Introduction to Boosting (XGBoost/Gradient Boosting)	Hands-on Labs: Regression and classification projects using Scikit-learn
Module 6: Unsupervised Learning Algorithms	5 Hours	Clustering (K-Means, Hierarchical Clustering) Dimensionality Reduction (PCA) Anomaly Detection Basics Association Rule Mining (Apriori Overview)	Hands-on Lab: Customer segmentation using clustering
Module 7: Model Evaluation, Tuning & Feature Engineering	4 Hours	Evaluation Metrics (Accuracy, Precision, Recall, F1-Score, ROC-AUC, RMSE/MAE) Confusion Matrix Interpretation Overfitting vs Underfitting & Bias-Variance Tradeoff Hyperparameter Tuning (GridSearchCV, RandomizedSearchCV) Feature Selection & Feature Engineering Techniques	Hands-on Lab: Tuning and comparing multiple models
Module 8: Deep Learning & Neural Networks	8 Hours	Neural Network Fundamentals (Perceptron, Activation Functions) Forward & Backward Propagation Gradient Descent Building ANN using TensorFlow/Keras Introduction to Convolutional Neural Networks (CNN) for Image Data Introduction to Recurrent Neural Networks (RNN) for Sequence Data Regularization (Dropout, Batch Normalization)	Hands-on Lab: Image classification using a CNN
Module 9: Natural Language Processing (NLP)	4 Hours	Text Preprocessing (Tokenization, Stemming, Lemmatization, Stop-Word Removal) Bag of Words, TF-IDF Introduction to Word Embeddings (Word2Vec Overview) Sentiment Analysis Project Overview of Transformer-Based Models (BERT/GPT Conceptual)	Hands-on Lab: Building a sentiment classifier
Module 10: Computer Vision	4 Hours	Image Processing Basics with OpenCV Image Classification Pipeline Recap Object Detection Concepts (Overview of YOLO/SSD) Transfer Learning Using Pre-Trained Models (e.g., MobileNet/ResNet)	Hands-on Lab: Transfer learning for a custom image classifier
Module 11: Model Deployment & MLOps Basics	4 Hours	Saving & Loading Models (Pickle, Joblib, Model Checkpoints) Building a REST API with Flask/FastAPI for Model Serving Introduction to Deployment Platforms (Streamlit, Cloud Basics) Overview of MLOps (Version Control, Model Monitoring, CI/CD Concepts)	Hands-on Lab: Deploying a trained model as a web app/API
Module 12: Capstone Project & Program Wrap-Up	4 Hours	End-to-End Project (Problem Statement, Data, Modeling, Evaluation, Deployment) Project Mentoring & Doubt Resolution Project Presentations by Participants Resume/Portfolio Guidance & Common ML Interview Questions Feedback Session & Certification	Capstone: Complete end-to-end ML solution

Total 60 Hours

Capstone Project (Included)

Participants build and present a complete end-to-end ML solution that includes:

Defining a real-world problem statement

Sourcing and preparing the dataset

Building and training the model

Evaluating and tuning model performance

Deploying the model as a web app or API

Presenting the project for feedback and certification

AI & Machine Learning Admission Process

1

Fill Inquiry Form

Share your details and course interest

2

Counselling Call

Speak with our admissions team about your goals and fit

3

Get Course Access

Receive login credentials and join the orientation session

Requirements

Eligibility Criteria: this is not required

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Learn From AI & Machine Learning Industry Experts

AL/ML

AI/ML Trainer

years of experience in Machine Learning and Deep Learning. Expert in Python, TensorFlow, Scikit-learn, and model deployment. Focuses on real-time projects and hands-on training to make students job-ready.

Python

TensorFlow

Scikit-learn

model deployment

Tools & Technologies You'll Master

Learning Outcomes

By the end of this AI and Machine Learning course, participants will be able to:

Understand core AI/ML concepts and where they apply in real-world problems

Clean, explore, and prepare data for machine learning

Build, evaluate, and tune classical ML models (regression, classification, clustering)

Design and train basic deep learning models for image and text data

Deploy a trained ML model as a working application or API

Present an end-to-end ML project confidently, ready for a portfolio or interview

Assessment & Certification

Module-end quizzes/assignments (formative assessment)

Mid-program mini-project (after Module 7)

Final capstone project with presentation (Module 12)

Testimonials

What Our AI & Machine Learning Learners Say

"The supervised learning and model tuning modules gave me a real feel for how ML gets built in practice, not just theory from a textbook. Presenting my capstone project in the final module was the moment I felt genuinely job-ready."

Rohan Kulkarni

Machine Learning Engineer at a fintech company

"I came in comfortable with Excel and SQL but nervous about Python. The Python and statistics modules built my confidence step by step, and by the deep learning module I was building CNNs on my own."

Ishita Bansal

Data Analyst transitioning into AI/M

"Strong course overall, especially the hands-on labs after every single topic. I'd have liked a little more time on NLP, but the capstone project more than made up for it when it came to interviews."

Aditya Menon

CS Graduate, now Junior ML Engineer

"The deployment module was what really differentiated this course for me. Learning to actually ship a model as an API instead of just training it in a notebook made my portfolio stand out."

Sneha Kulkarni

Working Professional switching into Data Science

"No prior ML background going in, just some basic Python. The way the modules built from statistics to classical ML to deep learning made everything click, and the mentorship during lab hours was genuinely useful."

Varun Chandra

Fresher, now AI Engineer

"Good structured program covering classical ML, deep learning, NLP, and computer vision all in one track. The mid-program mini-project was a great checkpoint to see how much I'd actually absorbed."

Priyanka Rathi

Analyst upskilling into Machine Learning